Medical-image processing apparatus, medical-image processing method, and program for the same

ABSTRACT

A medical-image processing apparatus according to the present invention includes an obtaining unit configured to obtain a medical image obtained by capturing an image of an examinee and a generation unit configured to input the medical image to a learning model selected based on an operation mode of a sensor at the image capturing to generate a medical image of a higher resolution than a resolution of the medical image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of International Patent ApplicationNo. PCT/JP2021/038606, filed Oct. 19, 2021, which claims the benefit ofJapanese Patent Application No. 2020-179042, filed Oct. 26, 2020, bothof which are hereby incorporated by reference herein in their entirety.

TECHNICAL FIELD

The present invention relates to a medical-image processing apparatus, amedical-image processing method, and a program for the same.

BACKGROUND ART

X-ray diagnosis and treatment based on radiography are widely performedin medical front, and digital diagnostic imaging based on radiographicimages captured using a radiation detector (hereinafter referred to as“sensor”) is in widespread use all over the world. The sensor can imageoutput immediately and can therefore capture not only still images butalso moving images. Furthermore, an increase in the resolution of thesensor allows imaging that provides more detailed information.

In contrast, reduced-resolution radiographic images are sometimesobtained to reduce radiation exposure to the examinee. One example is ause case in which X rays are applied for a long time, such as movingimages. In this case, the sensor increases X-ray dose per pixel byoperating using multiple pixels as one pixel. This allows the overallX-ray radiation to be reduced, thereby reducing radiation exposure tothe examinee.

However, the reduction in resolution causes loss of detailed informationin the radiographic images, such as lesion information and informationfor accurate positioning of the imaging apparatus.

One example of a process for decompressing detailed information inlow-resolution images (increasing the resolution) is superresolutionprocessing. Known examples of the superresolution processing include amethod for converting multiple low-resolution images to ahigh-resolution image and a method for associating the features of alow-resolution image with the features of a high-resolution image andproviding a high-resolution image on the basis of the information (PTL1). A recent example method for associating features is machinelearning. In particular, supervised learning using a convolutionalneural network (CNN) is rapidly becoming popular because of their highperformance (PTL 2). Superresolution processing using the CNNdecompresses detailed information in input low-resolution images usinglearning parameters created by means of supervised learning. Thesuperresolution processing is also applied to medical images.

Superresolution processing using the CNN makes an inference using alow-resolution image as an input and outputs a superresolution image asan inference. A high-resolution image is used as a training image fortraining. For this reason, multiple sets of a high-resolution image anda low-resolution image are prepared as training data. In learning, amethod for generating a low-resolution image from a high-resolutionimage is learned. However, a method for generating a low-resolutionimage from a high-resolution image varies according to the operatingmethod of the sensor. Using a CNN that has learned one generation methodand using a low-resolution image generated using another generationmethod as an input for inference will result in a decrease in thequality of the superresolution image.

CITATION LIST Patent Literature

-   PTL 1 Japanese Patent No. 4529804-   PTL 2 Japanese Patent No. 6276901

SUMMARY OF INVENTION

The present invention is made in view of the above problems, and anobject is to provide an apparatus and a method for processing medicalimages of appropriately improved resolution, and a program for the same.

Another object of the present invention is to offer operationaladvantages that are provided using the configurations of the followingembodiments and that are not provided using known techniques.

A medical-image processing apparatus according to the present inventionincludes an obtaining unit configured to obtain a medical image obtainedby capturing an image of an examinee and a generation unit configured toinput the medical image to a learning model selected based on anoperation mode of a sensor at the image capturing to generate a medicalimage of a higher resolution than a resolution of the medical image.

Further features of the present invention will become apparent from thefollowing description of exemplary embodiments with reference to theattached drawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an example of the functional configurationof a medical-image processing apparatus according to a first embodiment.

FIG. 2A is a diagram illustrating an example of the hardwareconfiguration of the medical-image processing apparatus according to thefirst embodiment.

FIG. 2B is a diagram illustrating an example of the hardwareconfiguration of the medical-image processing apparatus according to thefirst embodiment.

FIG. 3 is a flowchart of an example of the processing procedure of themedical-image processing apparatus according to the first embodiment.

FIG. 4 is a diagram illustrating an example of the selection screen ofthe medical-image processing apparatus according to the firstembodiment.

FIG. 5A is a flowchart of an example of the learning procedure of themedical-image processing apparatus according to the first embodiment.

FIG. 5B is a flowchart of an example of the learning procedure of themedical-image processing apparatus of the first embodiment.

FIG. 6A is a table showing the relationship between the operation modeof the sensor and the learning model of the medical-image processingapparatus according to the first embodiment.

FIGS. 6B (1), (2) and (3)are diagrams showing examples of an additionmethod according to the first embodiment.

FIG. 7A is a table showing the relationship between the operation modeof the sensor and the learning model of the medical-image processingapparatus according to a second embodiment.

FIG. 7B is a table showing the relationship between the operation modeof the sensor and the learning model of the medical-image processingapparatus according to the second embodiment.

DESCRIPTION OF EMBODIMENTS

The following embodiments illustrate a representative example in whichradiographic images are used as an example of medical images. Morespecifically, an example in which radiographic images obtained usingsimple roentgenography are used as an example of the radiographic imageswill be described. The medical images applicable to the embodiments areillustrative only, and other medical images can also be suitablyapplied. Examples include medical images obtained using a computedtomography (CT) scanner, a magnetic resonance imaging (MRI) scanner, athree-dimensional ultrasonic imaging system, a photoacoustic tomographyscanner, a positron emission tomography (PET)/single photon emissioncomputed tomography (SPECT) scanner, an optical coherence tomography(OCT) scanner, and a digital radiography scanner.

The following embodiments illustrate building of a learning model basedon supervised learning using a convolutional neural network (CNN) inwhich a low-resolution medical image, which is input data, and ahigh-resolution medical image, which serves as correct data, are used astraining data. For this reason, the learning model is hereinafterreferred to as CNN. Not the learning using the CNN but any machinelearning capable of building a learning model capable of outputtingmedical images with improved resolution and reduced noise may be used.

First Embodiment

A medical-image processing apparatus according to this embodiment inputsa medical image to a learning model selected on the basis of theoperation mode of a sensor used for capturing the medical image andgenerates a medical image of a resolution higher than that of themedical image.

FIG. 1 is a block diagram of a medical-image processing apparatus 100according to the present invention. The medical-image processingapparatus 100 includes a learning-model selecting unit 101, an imageobtaining unit 102, and a machine learning unit 103.

The learning-model selecting unit 101 obtains the operation mode of thesensor and outputs a learning model for machine learning. The imageobtaining unit 102 obtains a radiographic image from an external deviceand outputs a low-resolution radiographic image. The machine learningunit 103 receives the low-resolution radiographic image and the learningmodel for machine learning as an input and performs inference processingof superresolution processing CNN and output a superresolution image.

FIGS. 2A and 2B illustrate the hardware configuration of FIG. 1 . In theconfiguration example in FIG. 2A, a radiographic image needed forlearning is obtained. A control personal computer (PC) 201 and an X-raysensor 202, such as a flat panel sensor, which converts an X-ray signalto a digital image and output it are connected by a Gigabit Ether 204.The signal line may be not the Gigabit Ether but a controller areanetwork (CAN) or an optical fiber. The Gigabit Ether 204 connects to anX-ray generating apparatus 203, a display 205, a storage 206, a networkinterface 207, an ion chamber 210, and an X-ray control unit 211. Thecontrol PC 201 is configured such that, for example, a centralprocessing unit (CPU) 2012, a random access memory (RAM) 2013, a readonly memory (ROM) 2014, and a storage 2015 are connected to a bus 2011.The control PC 201 connects to an input unit 208 using a universalserial bus (USB) or a PS/2 port and connects to a display 209 using aDisplayPort or a digital visual interface (DVI). The control PC 201 isused to send commands to the X-ray sensor 202 and the display 205. Inthe control PC 201, processing details for each image-capturing mode arestored in the storage 2015 as software modules. The processing detailsare read to the RAM 2013 according to instruction means (not shown) forexecution. The processed image is sent to the storage 2015 in thecontrol PC 201 or the storage 206 outside the control PC 201 forstorage.

The learning-model selecting unit 101, the image obtaining unit 102, andthe machine learning unit 103 shown in FIG. 1 are stored in a storage2215 as software modules. It is needless to say that the learning-modelselecting unit 101, the image obtaining unit 102, and the machinelearning unit 103 shown in FIG. 1 may be implemented as a dedicatedimage processing board. Optimum implementation for the purpose may beperformed.

In the configuration example of FIG. 2B, the training of the CNN isperformed. A learning PC 221 is configured such that, for example, a CPU2212, a RAM 2213, a ROM 2214, and a storage 2215 are connected to a bus2211. The learning PC 201 connects to an input unit 222 using a USB orPS/2, connects to a display 223 using DisplayPort or DVI, and connectsto a storage 224 using a USB. In training the CNN, the machine learningunit 103 shown in FIG. 1 is stored in the storage 2215 as a softwaremodule. It is needless to say that the machine learning unit 103 shownin FIG. 1 may be implemented as a dedicated image processing board.Optimum implementation for the purpose may be performed.

The processing will be described with reference to the functional blockdiagram in FIG. 1 and the flowchart in FIG. 3 illustrating the overallprocessing procedure.

S301: Obtaining Operation Mode of Sensor

First at S301, the learning-model selecting unit 101 obtains theoperation mode of the sensor in capturing an image of the examinee. Theoperation mode of the sensor is a method whereby the sensor generates animage and outputs it. Examples of the operation mode include a binningcount, a method of adding pixels in the binning area, and a frame rate.

S302: Selecting Learning Model

Next at S302, the learning-model selecting unit 101 selects a learningmodel on the basis of the operation mode of the sensor. The learningmodel is a training parameter set of the CNN that has performedsupervised learning in advance. The association of the operation mode ofthe sensor with the learning model is set in advance. More specifically,the operation mode of the sensor and the learning model trained inadvance using an image captured in the same operation mode as theoperation mode of the sensor are associated with each other and is set.For example, a setting screen, as shown in FIG. 4 , is displayed, andthe user may set the association on the basis of the display. Thelearning model is set together with the operation mode of the sensor,such a binning count and a frame rate. The above setting method isillustrative only. Any method for associating the operation mode of thesensor with the learning model may be employed. For example, ifadditional information on the operation mode is associated with amedical image for use in training the learning model, the informationmay be read and associated with the learning model. A method fordisplaying images may be set in association with the operation mode ofthe sensor and the learning model.

The operation of the CNN at the training will be described withreference to FIGS. 5A and 5B.

S501: Inference Processing

At S501, the machine learning unit 103 builds a learning model byperforming supervised learning using a set of input data and output dataas training data. The training data is a set of low-resolution images511, or input data, and high-resolution images 515, or correct datacorresponding thereto. For the low-resolution images 511 and thehigh-resolution images 515 for use as training data, for example, themachine learning unit 103 converts the resolution of the high-resolutionimages 515 to generate the low-resolution images 511 of a lowerresolution than the resolution of the high-resolution images 515. Theresolution of the high-resolution images 515 subjected to a noisereduction process in advance may be converted to generate thelow-resolution images 511 of reduced noise.

The machine learning unit 103 performs inference processing on thelow-resolution images 511 using the parameters of a CNN 512 in thecourse of learning and outputs superresolution images 514 as inferences(S501). The CNN 512 has a structure in which multiple processing units513 are freely connected. Example processes performed by the processingunits 513 include a convolutional operation, a normalization process,and processes using an activating function such as ReLU or Sigmoid, forwhich a parameter set for describing the individual processing detailsis present. The parameters can take various structures. For example,parameter sets are connected in about three to hundreds layers in theorder of convolutional operation, normalization, and activatingfunctions.

S502: Calculating Loss Function

Next at S502, the machine learning unit 103 calculates a loss functionfrom the superresolution images 514, which are inferences, and thehigh-resolution images 515. The loss function may be any function, suchas a square error or a cross entropy error.

S503: Error Backpropagation

Next at S503, the machine learning unit 103 performs errorbackpropagation starting from the loss function calculated at S502 toupdate the parameter set of the CNN 512.

S504: Determining Whether to End Learning

Finally at S504, the machine learning unit 103 determines whether to endthe learning, and if the learning is to be continued, goes to S501.Repeating the processes from S501 to 503 while changing thelow-resolution images 511 and the high-resolution images 515 allows theupdate of the parameters of the CNN 512 to be repeated so that the lossfunction is decreased, thereby increasing the accuracy of the machinelearning unit 103. When the learning is sufficiently advanced and isdetermined to be ended, the process is completed. The determinationwhether to end the learning is performed on the basis of criteria setfor the problems, for example, that the accuracy of the inference hasreached a fixed value or greater without occurrence of over-training orthat the loss function has reached a fixed value or less.

Thus, the training of the CNN is performed.

Examples of a combination of training parameters and the operation modeof the sensor are shown in FIG. 6A. A binning count and an additionmethod are shown as examples of the operation mode of the sensor. Thebinning process is a process of adding signals of multiple pixels of theX-ray sensor 202 to output the added values as a signal of one pixel. Abinning count M refers to outputting an area of M × M as one pixel. Inother words, a binning count 2 refers to outputting four pixels in a 2 ×2 area as one pixel. The binning area may be M × N (N is a numberdifferent from M). The addition method refers to group pixels to onepixel in binning. FIGS. 6B(1), (2), and (3) show examples of theaddition method when the binning count is 2. The pixel with a circle isused in size reduction. For thinning, one pixel in the 2 × 2 area isused. For full addition, all the pixels are used. For diagonal addition,pixels to be used are diagonally selected. For addition, such as fulladdition or diagonal addition, the sum may be divided by the additioncount to average them to make the pixel values equal. Filtering may beperformed before the addition to prevent aliasing. If the binning countor the addition method differs, the process for generatinghigh-resolution images from low-resolution images also differs, andtherefore the content of learning of the CNN also differs. This requiresto generate parameters by learning with a set of training data of thelow-resolution images 511 and the high-resolution images 515 shown inFIG. 5B.

As shown in FIG. 6A, the operation mode of the sensor is selected todetermine which parameter is to be used. The operation mode of thesensor is determined, for example, at the timing when the method ofimage capturing is determined. The method of image capturing is linkedto the technique of image capturing. For this reason, if one techniquefor image capturing is selected, image capturing conditions and theoperation mode are determined. Accordingly, the learning-model selectingunit 101 loads parameters to be used and applies data to a necessarymemory area at the timing when the image capturing technique isdetermined. If there is room in the memory area, all the parameters maybe loaded in advance, for example, at the start of the apparatus, andthe data references may be changed at the timing when the technique forimage capturing is determined.

S303: Acquiring Radiographic Image

Next at S303, the image obtaining unit 102 obtains an image from theX-ray sensor.

S304: Preprocessing

Next at S304, the image obtaining unit 102 preprocesses the obtainedimage to output a preprocessed image. The preprocessing is processingfor preparing for superresolution processing. For example, the imageobtaining unit 102 performs at least one of processing for correctingthe characteristics of the sensor, frequency processing, and gradationprocessing. In the processing for correcting the characteristics of thesensor, offset correction, (dark-current correction), gain correction,and loss correction are performed to keep correlation with theperipheral pixels.

S305: Superresolution Processing

Finally at S305, the machine learning unit 103 receives the preprocessedimage as an input, performs CNN inference processing using the learningmodel selected at S302, and outputs a superresolution image.

Thus, the processing of the medical-image processing apparatus 100 isperformed.

As described above, a learning model is selected using a medical image,which is captured on the basis of the operation mode of the sensor atthe image capturing, as an input, and a resolution-increased medicalimage as an output. The selected learning model has learned a medicalimage, in advance, captured in the same operation mode as the operationmode of the sensor at the image capturing. This matches the generationmethod for the input medical image with the generation method for themedical image used in training the learning model, allowing generationof a medical image with appropriated improved resolution.

In this embodiment, the addition method and the binning count are usedas examples of the operation mode of the sensor. Alternative examplesinclude the image obtaining rate (frame rate) and the reading area sizeof the sensor and other items related to a change in the sensoroperation method. The operation mode of the sensor may be changed notonly in a single sensor but also across a plurality of sensors. If thesame addition method applies to the same sensor, the learning model ischanged for each sensor.

There is no need to prepare different learning models for all operationmodes. If a sensor operation mode that can be shared, such as anoperation mode in which the process of generating high-resolution imagesfrom low-resolution images is the same, the same learning model may beused among the operation modes of the sensor.

Second Embodiment

Another embodiment of the learning model setting different from S302 inthe first embodiment will be described with reference to the blockdiagram in FIG. 1 and the overall flowchart in FIG. 3 .

S301: Obtaining Operation Mode of Sensor

First at S301, the learning-model selecting unit 101 obtains theoperation mode of the sensor. The operation mode of the sensor is apattern indicating how the sensor generates and outputs an image.

S302: Selecting Learning Model

At S302, the learning-model selecting unit 101 selects a learning modelon the basis of the operation mode of the sensor. The learning modelincludes a learning network (CNN) that performed supervised learning inadvance and CNN training parameters obtained by learning.

The operation of the CNN at the learning is the same as that of thefirst embodiment, and a description thereof is omitted. Examples of acombination of the learning model and the operation mode of the sensorare shown in FIG. 7A. An example of the operation mode of the sensor isa binning count. Increasing the binning count can increase the readingrate of the sensor, and the binning count is used for image capturingthat requires a high frame rate. This requires higher performance forthe CNN. In other words, different binning counts require differentperformance. For this reason, a CNN with faster processing speed isselected for the operation mode of a sensor with a greater binningcount. In other words, for a second operation mode with higher operationspeed than a first operation mode, a second learning model with higherprocessing speed than that of a first learning model associated with thefirst operation mode is set in association therewith.

For example, the number of processing units 513 constituting the CNN 512in FIG. 5B may be changed for each binning. The processing speed isincreased by reducing the number of processing units 513 as the binningcount increases. Alternatively, the processing speed may be increased bydecreasing the number input/output parameters for convolutionaloperation performed by the processing units 513 although the number ofprocessing units 513 is kept unchanged. The number of parameters may bedecreased by reducing the size of convolutional operation or reducingthe number of output channels.

The steps from S303 to S305 are the same as those of the firstembodiment, and descriptions thereof are omitted.

In this embodiment, the binning count is used as an example of theoperation mode of the sensor. The same applies to another operation modeof the sensor related to an increase in the sensor operation speed.

Third Embodiment

Another embodiment of the learning model setting different from S302 ofthe first embodiment will be described with reference to the blockdiagram of FIG. 1 and the overall flowchart of FIG. 3 .

S301: Obtaining Operation Mode of Sensor

At S301, the learning-model selecting unit 101 obtains the operationmode of the sensor. The operation mode of the sensor is a patternindicating how the sensor generates and outputs an image.

S302: Setting Learning Model

At S302, the learning-model selecting unit 101 obtains a learning modelon the basis of the operation mode of the sensor. The learning modelincludes the training parameters of the CNN that performed supervisedlearning in advance.

The operation of the CNN at the learning is the same as that of thefirst embodiment, and a description thereof is omitted. Examples of acombination of the learning model and the operation mode of the sensorare shown in FIG. 7B. An example of the operation mode of the sensor isan addition method. The difficulty in the expression of the CNN variesaccording to the addition method, and as a consequence, the degree ofconvergence of CNN learning varies. For this reason, the hyperparametersare changed according to the addition method to provide the optimumconvergence without fluctuating the loss curve. One example of thehyperparameter is a training rate. The training rate is anerror-reflected parameter, which is determined as follows. A gradientdescent method is generally used to determine the parameters of the CNN412. The parameter W of the CNN 412 is updated in the gradient descentmethod, as expressed as Eq. 1.

W : = W−  α∇J(W)

where J is an error in the parameter W, := is assignment operation, ∇ isgradient, and α is a training rate. Decreasing the value of α decreasesthe reflection of the error J on the parameter W, and increasing thevalue of α increases the reflection of the error J on the parameter W.Accordingly, for the addition method in which the loss curve fluctuates,the reflection of the error is decreased by decreasing the trainingparameter.

Steps from S303 to S305 are the same as those of the first embodiment,and descriptions thereof are omitted.

Although this embodiment uses the training rate as the hyperparameter, abatch size or an epoch count may be used.

Other Embodiments

It is to be understood that the present invention can also beimplemented by supplying a program for implementing one or morefunctions of the above embodiments to a system or an apparatus via anetwork or a storage medium and by reading and executing the programwith one or more processors of the computer of the system or theapparatus. The present invention can also be implemented by a circuitfor performing one or more of the functions.

The processor or the circuit can include a central processing unit(CPU), a microprocessing unit (MPU), a graphics processing unit (GPU),an application specific integrated circuit (ASIC), and a fieldprogrammable gateway (FPGA). The processor or the circuit can include adigital signal processor (DSP), a data flow processor (DFP), and aneural processing unit (NPU).

The medical-image processing apparatuses according to the embodimentsmay be realized as stand-alone apparatuses or may be a communicablecombination of a plurality of apparatuses combined so as to execute theabove processes, both of which are included in the embodiments of thepresent invention. The above processes may be executed by a commonserver or a server group. The plurality of units constituting eachmedical-image processing apparatus only needs to be able to communicatewith one another at a predetermined communication rate and does not haveto be present in the same facility or in the same country.

The embodiments of the present invention include a configuration inwhich a program of software for implementing the functions of the aboveembodiments is supplied to a system or an apparatus and the computer ofthe system or the apparatus reads and executes the code of the suppliedprogram.

Accordingly, the program code installed in a computer to implement theprocesses according to the embodiments is also one of the embodiments ofthe present invention. The functions of the embodiments can also beimplemented by part or all of the actual processes performed by anoperating system (OS) operating in the computer according toinstructions included in a program read by the computer.

The present invention allows generation of a medical image ofappropriately improved resolution.

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

1. A medical-image processing apparatus comprising: an obtaining unitconfigured to obtain a medical image obtained by capturing an image ofan examinee; and a generation unit configured to input the medical imageto a learning model selected based on an operation mode of a sensor atthe image capturing to generate a medical image of a higher resolutionthan a resolution of the medical image.
 2. The medical-image processingapparatus according to claim 1, wherein the operation mode comprises atleast one item of a binning count and a frame rate, and wherein thegeneration unit inputs the medical image to the learning model selectedbased on the at least one item.
 3. The medical-image processingapparatus according to claim 1, wherein the generation unit generatesthe medical image of a higher resolution than a resolution of themedical image obtained by the obtaining unit using a learning model thathas learned training data including a medical image of a firstresolution and a medical image of a second resolution lower than thefirst resolution, the medical image of the second resolution beingobtained by converting the resolution of the medical image of the firstresolution.
 4. The medical-image processing apparatus according to claim3, wherein the generation unit generates the medical image of a higherresolution than the resolution of the medical image obtained by theobtaining unit using a learning model that has learned training dataincluding a medical image of the first resolution subjected to a noisereduction process and a medical image of the second resolution lowerthan the first resolution, the medical image of the second resolutionbeing obtained by converting the resolution of the medical image of thefirst resolution subjected to the noise reduction process.
 5. Themedical-image processing apparatus according to claim 1, furthercomprising: a setting unit configured to associate the operation mode ofthe sensor with the learning model selected based on the operation modeof the sensor.
 6. The medical-image processing apparatus according toclaim 5, wherein the setting unit associates the operation mode of thesensor with the learning model that is trained in advance using an imagecaptured in the same mode as the operation mode of the sensor.
 7. Themedical-image processing apparatus according to claim 5, wherein thesetting unit associates the operation mode including at least one of thebinning count and the frame rate with the learning model receives themedical image captured at the operation mode, and wherein the generationunit inputs the medical image to the learning model selected based onrelationship between the operation mode and the learning model set bythe setting unit.
 8. The medical-image processing apparatus according toclaim 7, wherein the setting unit associates a second operation modewith a higher sensor operation speed than an operation speed of a firstoperation mode with a second learning model with a higher processingspeed than a processing speed of a first learning model set inassociation with the first operation mode.
 9. The medical-imageprocessing apparatus according to claim 7, wherein the setting unitassociates an operation mode with a second binning count greater than afirst binning count with a second learning model with a higherprocessing speed than a processing speed of a first learning model setin association with the operation mode with the first binning count. 10.The medical-image processing apparatus according to claim 5, wherein thesetting unit further sets a method for displaying the medical imagegenerated by the generation unit.
 11. The medical-image processingapparatus according to claim 5, wherein the setting unit further sets amethod for adding a plurality of pixels of the sensor in a binningprocess of adding outputs of signals of the plurality of pixels andoutputting the outputs as a signal of one pixel.
 12. The medical-imageprocessing apparatus according to claim 1, wherein the medical imagecomprises a radiographic image.
 13. A medical-image processing apparatuscomprising: an obtaining unit configured to obtain a radiographic imageobtained by capturing an image of an examinee using a radiationdetector; a setting unit configured to associate an operation mode ofthe radiation detector with a learning model that is trained in advanceusing a radiographic image captured at the operation mode; and ageneration unit configured to input the radiographic image obtained bythe obtaining unit to the learning model associated with the operationmode of the radiation detector in capturing the image of the examinee,the leaning model being selected based on the setting, to generate aradiographic image of a higher resolution than a resolution of theradiographic image.
 14. A medical-image processing method comprising:obtaining step of obtaining a medical image obtained by capturing animage of an examinee; and generation step of inputting the medical imageto a learning model selected based on an operation mode of a sensor atthe image capturing to generate a medical image of a higher resolutionthan a resolution of the medical image.
 15. A non-transitorycomputer-readable storage medium storing a program for causing acomputer to execute the method according to claim 14.